Mercedes-Benz had a legacy codebase modernization that was projected to take 8 months. When they handed it to Devin, an autonomous AI software engineer, it was done in 8 days.
Remember when AI coding tools were dismissed as "just glorified autocomplete"? The enterprise numbers coming out in 2026 make that reaction feel like ancient history.
How is 8 days even possible?
Devin launched in 2024 billing itself as "the world's first autonomous AI software engineer." At the time, that sounded like marketing copy. Two years in, the direction has proven correct.
The fundamental difference from existing AI coding tools: GitHub Copilot and Cursor work alongside a developer — suggesting code, completing functions, answering questions. The developer's hands stay on the keyboard. Devin operates differently. Give it a task list and it works through to completion on its own. It opens a browser, runs terminal commands, edits code, and opens PRs on GitHub. If it hits a wall, it searches the web for a solution.
Technically it runs inside an isolated sandboxed VM, operating like a real developer with access to a browser, terminal, and code editor — plus integrations with 50+ tools including GitHub, Linear, Slack, Jira, AWS, and Datadog.
- Assign a task
Give it an instruction like "migrate this legacy codebase to Node.js 18" - Autonomous execution
Devin analyzes the code, maps dependencies, and implements changes - PR submission
When done, it creates a branch and opens a PR automatically - Review and merge
A human reviews the PR — this is the only step requiring human involvement
On SWE-bench (a benchmark measuring the ability to resolve real GitHub issues), Devin 2.0 scores 45.8%. That's more than triple the 13.86% of the original 2024 version, and the number is still climbing.
What's actually different from autocomplete?
One-liner: Copilot helps you do the work. Devin does the work instead of you.
| Code assistant (Copilot/Cursor) | Autonomous AI agent (Devin) | |
|---|---|---|
| How it works | Suggests code next to you | Completes entire tasks independently |
| Human involvement | Every step | Task kickoff + PR review only |
| Best for | Writing code, debugging | Migrations, repetitive automation |
| Ideal user | Individual developers | Teams / Enterprise |
| Price | $10–$40/month | Team plan $500/month |
The enterprise case studies make the difference concrete.
Nubank is the standout case. Brazil's largest digital bank deployed Devin to migrate a 6M+ line legacy codebase — work that would have taken months or years — completing it in weeks with 20x cost savings and 8–12x engineering efficiency gains.
Cognition's own CEO Scott Wu confirmed that more than 90% of the company's code is now written by Devin. An AI coding tool company building itself with its own tool — a natural fit that is, remarkably, actually working at scale.
"AI is fundamentally transforming how software is built. At Cognizant, 30 percent of our code is already generated with AI, and we aim to reach 50 percent in the near future."
— Ravi Kumar S., CEO of Cognizant
How to get started: the essentials
- Sign up at devin.ai
Team plan is $500/month. For a team, calculate the ROI against engineering hours on repetitive work — the math tends to work out quickly. - Start with safe, bounded tasks
Pick tasks with clear scope and easy verification. "Write tests for this function" is a good first task. - Write specific instructions
"Improve this code" (❌) → "Refactor the Express routers in src/api/ and bring test coverage above 80%" (✅). Scope clarity directly correlates with output quality. - Set up a PR review routine
Devin's PRs always need a human review. It's autonomous execution, but final judgment stays with you. - Build a tech debt queue
List out your team's backlog of avoided work. Migrations, security patches, dependency updates. Feed Devin those tasks one by one — that's where ROI is highest.
Tasks where Devin excels
Legacy code migrations / Test coverage expansion / Security vulnerability patching / Dependency updates / API integration / Documentation. For new architecture decisions or complex business logic, human judgment is still needed.
